AI’s Dual Nature: Reasoning Models Emerge as Key Differentiator for Business

People who have used ChatGPT know what generative AI is: They type or speak a prompt, and the chatbot generates an answer, whether it is text, an image, a video, a piece of code, music or 3D content.

But in recent months, artificial intelligence (AI) leaders such as OpenAI, Google, Anthropic and Amazon have been releasing reasoning models as well. DeepSeek, the Chinese AI startup that tanked Nvidia’s stock in one day, also has both a generative AI chatbot and a reasoning AI model.

What is a reasoning model, how is it different from generative AI — and why is the distinction important to business?

Reasoning models are systems that not only generate outputs, but also think through answers, evaluate thinking paths and solve problems. While both generative and reasoning models fall under the umbrella of AI, their goals, architectures and applications differ.

PYMNTS Intelligence data shows that businesses are finding more use cases for generative AI and seeing positive results from their investments. In a recent survey of 60 chief financial officers (CFOs) at U.S. firms that made at least $1 billion in revenue last year, 90% reported positive returns on investment.

Understanding the distinction between reasoning and generative AI models is crucial for selecting the right tool for a task.

A marketing copywriter may benefit from generative models that can come up with creative ideas for an ad campaign, but a financial analyst needs an AI that can reason through facts, evaluate trade-offs and mitigate errors.

Companies can also combine the use of both models for one task by leveraging the strengths of each to come up with a better result.

For example, a generative AI chatbot generates answers for a human customer service representative to give to a consumer while a reasoning model checks the answers for compliance with company guidelines.

Read more: This Week in AI: DeepSeek Hits Chip Stocks, Meta Stays Pat on AI Spending, SoftBank Invests in OpenAI

What Are Reasoning Models?

Reasoning models in AI are designed to mimic the way humans engage in logical thinking, decision-making and problem-solving. While generative AI models like ChatGPT use pattern recognition — looking at sequences of content to make a calculated guess as to what comes next — reasoning models take it one step further.

They follow steps of logic, make inferences and explain how they reach conclusions. Their primary purpose is not just to generate content, but to reason through a problem space, whether that involves analyzing facts, answering complex questions or solving multi-step tasks.

That’s why reasoning model like OpenAI’s o1 and o3-mini AI models tend to take longer before answering.

As an example, a 1,000-word document was uploaded to ChatGPT for double-checking and o1 was the model selected for the task. It took one minute to think before answering. The user could see the stages: Reasoning, example reasoning, tracing AI reasoning, harnessing hybrid techniques, advancing reasoning strategies, creating and enhancing, pinpointing differences, enhancing precision and other steps.

Using a generative AI model would have taken only a few seconds, since there would be no reasoning steps.

See also: Google Debuts Touted Gemini 2.5 in the ‘Winner-Take-All’ AI Model Race

What Are Generative AI Models?

Generative AI models generate new content based on input prompts. This could include generating text, images, audio, video and code. They use deep learning architectures, especially transformer-based models, which are trained on massive datasets to recognize and reproduce patterns.

The most well-known generative AI models today include OpenAI’s GPT-4, Anthropic’s Claude, Google’s Gemini and Meta’s LLama that excel at text and coding tasks. They are also increasingly multimodal, able to handle images and code, not just text. Pure text-to-image generation models include Midjourney, DALL-E and Stable Diffusion.

While generative AI models can produce jaw-dropping content, they lack understanding or reasoning capabilities. They predict what comes next in a sequence of tokens or pixels, based on statistical likelihood, rather than logic. This can more readily lead to hallucinations or inaccuracies.

Read more: Meta Expands Voice-Powered AI With Llama 4

Other Differences Between Models

Both types of models use similar underlying technologies, but their structure and purposes differ. Generative models’ main superpower is producing fluent or creative content; reasoning models focus on accuracy and logic.

This makes reasoning models better suited for highly regulated industries like healthcare, where understanding the reason behind an answer, like a medical diagnosis, is important.

However, generative models are being injected with reasoning capabilities to battle hallucinations. This is done using the following techniques:

Chain-of-thought prompting: The model goes through step-by-step reasoning steps.

Retrieval-augmented generation (RAG): Letting the model work off only select information before generating an answer.

Tools: Letting models invoke calculators, search engines or code interpreters.

Agentic AI frameworks: Turning models into autonomous agents that plan, reason and act.

For now, businesses should consider using both types of models. By using generative models for creativity and reasoning models for verification, companies can get the best of both worlds: Speed, creativity and accuracy.

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